Can I use these schemas for multi-tenant applications?

Absolutely, the skill provides organization-based models with built-in Row Level Security (RLS) policies to ensure strict data isolation between tenants.

Are the schemas validated before deployment?

Yes, the skill includes validation scripts that check for proper naming conventions, primary keys, foreign key relationships, and index optimization.

Does this skill support vector embeddings?

Yes, it includes specialized RAG patterns that utilize the pgvector extension and HNSW indexing for efficient similarity searches within Supabase.

How do I apply these patterns to my Supabase project?

The skill features automated migration scripts that generate timestamped SQL files and apply them directly to your database environment.

Does it track AI token usage and costs?

Yes, it includes a dedicated schema for tracking token usage, API costs, and rate limits to help you manage your AI infrastructure overhead.

AI Database Schema Patterns for Supabase

Name: AI Database Schema Patterns for Supabase
Author: vanman2024

byvanman2024

Gestión de Bases de Datos

Generates and manages production-ready PostgreSQL schemas for AI applications, including RAG storage, chat systems, and multi-tenant architectures.

Acerca de

This skill provides a comprehensive library of optimized database patterns specifically designed for modern AI development on Supabase. It automates the creation of complex schemas for Retrieval-Augmented Generation (using pgvector), conversation histories, and AI usage tracking, ensuring best practices like Row Level Security (RLS), HNSW indexing, and multi-tenant isolation are implemented correctly from day one. It streamlines the workflow from schema generation and validation to migration and data seeding.

Características Principales

Organization-based multi-tenancy with built-in RLS security policies
AI usage tracking for token counting, cost management, and rate limiting
0 GitHub stars
Pre-configured RAG document storage with pgvector and HNSW indexing
Production-ready chat schemas with participant tracking and full-text search
Automated schema validation and timestamped migration workflows

Casos de Uso

Implementing a RAG pipeline with optimized vector similarity search
Developing a real-time AI chat application with persistent message history
Building a multi-tenant AI SaaS with per-organization data isolation